Web-based fault diagnostic and learning system

被引：39

作者：

Ong S.K. ^{[1
]}

An N. ^{[1
]}

Nee A.Y.C. ^{[1
]}

机构：

[1] Department of Mechanical Engineering, National University of Singapore, Singapore

来源：

International Journal of Advanced Manufacturing Technology | 2001年 / 18卷 / 07期

关键词：

Expert systems; Fault diagnosis; Knowledge acquisition; Multi-agent sysems;

D O I：

10.1007/s0017010180502

中图分类号：

学科分类号：

摘要：

Web-based technology holds great potential for enabling the rapid dissemination of information and facilitating distributed decision-making. This paper presents a novel knowledge-based multi-agent system for remote fault diagnosis, which is composed of diagnostic and learning agents (DLAs), machine agents (MAs) and a central management agent (CMA). Machines are remotely diagnosed by the DLAs through the communication channels between the MAs and the DLAs. In addition, the DLAs can learn new expertise front the users, and the CMA can update the central knowledge base (CKB) shared by all the DIAs with the valuable expertise. When faults that cannot be solved with the present knowledge base occur, the DLA can acquire new knowledge, translate it into rules using a rule builder, and update the rules into the CKB. The CKB will become mature through a continuous learning process. A prototype system has been developed and used for remote fault diagnostics of tool wear in computer numerically controlled (CNC) machining.

引用

页码：502 / 511

页数：9

共 18 条

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